Computational Thinking - AI4kids

According to the curriculum design of the Ministry of Education, the purpose of computational thinking is to cultivate children, learn and understand the thinking logic of computational tools from programming, and use this method to analyze problems, develop research methods, and make effective decisions. Other literature has also proposed different explanations for computational thinking: a type of thinking that can use computers to solve problems, including the ability to use concepts such as abstraction, recursion, and iteration to process and analyze data and produce physical and virtual works (CSTA Computer Science Teachers Association, 2011). Thinking to design and implement algorithms to solve problems using digital technologies (ACARA Australian Curriculum, Assessment and Reporting Authority, 2013).

Jeannette M. Wing, a professor at Carnegie Mellon University in the United States, believes that computational thinking should be incorporated into basic language skills. In addition to reading, writing, and arithmetic, the concept of computational thinking should also be added: "The skills of computational thinking are not the exclusive domain of computer scientists, but are the abilities and qualities that everyone should possess."

Image source: Ministry of Education 2019 Information Technology Syllabus

According to Google, computational thinking includes:

• Abstraction: Identify items and extract relevant information to define key concepts
• Algorithm design: generating program instructions to solve a problem or complete a task
• Automation: Using computers or machines to complete repetitive tasks
• Data analysis: understanding data by developing inductive models or in-depth analytical methods
• Data collection: Collect relevant information to solve the problem
• Data presentation: Use appropriate charts, text or pictures to express and organize data
• Analysis: Breaking down data, procedures, and problems into smaller, more manageable parts
• Parallelization: Processing smaller tasks in a larger task simultaneously to efficiently achieve the desired result.
• Pattern generalization: models, rules, principles, or theories that generate patterns of observation to predict outcomes
• Pattern recognition: observing patterns, trends, or regularities in data
• Simulation: Develop models to mimic the rules of the real world

Source: Google (2015). Exploring Computational Thinking. Retrieved from

https://www.google.com/edu/resources/programs/exploring-computational-thinking/

Abstraction:

• Identify and extract the key parts related to problem solving, extract the basic problem solving unit, reuse this problem solving unit, and expand the problem solving domain → pattern recognition/generalization
• Mapping from the complex real world to a simplified abstract model → Modeling and simulation

Abstraction example:

• MRT Map
• Garbage classification illustration
• Abstraction of Operations
• Mind map

Pattern generalization:

• Produce common patterns, rules, principles or theories

  Modelling:

  • According to different needs (for easy understanding, definition, quantification, visualization or simulation, etc.), complex phenomena are expressed in a simplified way.
  • Can be used to visualize abstract concepts
  • Can be used as a basis for interpreting experimental results
  • Can serve as a basis for prediction

  Algorithmic thinking:

  Produce ordered instructions to solve a problem or complete a task

• Image source: http://gaymarriagedata.blogspot.com/2016/11/lifa.htmlData representation

• • Use appropriate charts, text or pictures to express and organize information
  • 

  • Examples of computational thinking

    • In the fields of science and engineering, computing is used to simulate building structures to confirm safety and to predict weather conditions to increase accuracy.
    • Humanities and social sciences Use computing to analyze and optimize advertising strategies Use computing to analyze population aging trends and medical resource distribution
    • Art fieldUsing computing to construct 3D animationUsing computing to create digital music

    Key Concepts

    • Problem Analysis
    • Abstraction
    • Pattern Recognition
    • Algorithm Design Indicators of students’ learning ability at different stages
    • Elementary school first and second grade:
    • • Observe the rules around us, such as leaves turning green and red, and falling leaves.
      • Use hand drawing to describe a story, for example: What did you do this morning?
      • Mixing colors with paint, and thinking about how the order of the paint affects the color

      Elementary school third to sixth grade:

      • Describe examples of computational thinking and discuss problem solving in real life
      • Present data using bar graphs, pie charts, sets, series, charts, etc.
      • Do long division, factorization, and carry in addition and subtraction

      Junior high school 1st to 3rd grade:

      • Use algebraic variables; identify basic facts in word problems; study algebraic functions and compare them with formula functions; use iteration to solve word problems
      • Define objects and methods; define main and functions
      • Implementing algorithms to conduct experiments on problems in a certain field

      High school first to third year:

      • Use data structures such as arrays, linked lists, stacks, queues, graphs, hash tables, etc.
      • Use procedures to encapsulate a set of frequently used instructions, use functions, use conditional statements, loops, recursion, etc.
      • Experiment and understand simple computational thinking

      Tag: 108 Curriculum , AI , Artificial Intelligence , Computational Thinking

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